Abstract [en]

This paper introduces a Context-aware Privacy Policy Language (CPPL) that enables mobile users to control who can access their context information, at what detail, and in which situation by specifying their context-aware privacy rules. Context-aware privacy rules map a set of privacy rules to one or more user's situations, in which these rules are valid. Each time a user's situation changes, a list of valid rules is updated, leaving only a subset of the specified rules to be evaluated by a privacy framework upon arrival of a context query. In the existing context-dependent privacy policy languages a user's context is used as an additional condition parameter in a privacy rule, thus all the specified privacy rules have to be evaluated when a request to access a user's context arrives. Keeping the number of rules that need to be evaluated small is important because evaluation of a large number of privacy rules can potentially increase the response time to a context query. CPPL also enables rules to be defined based on a user's social relationship with a context requestor, which reduces the number of rules that need to be defined by a user and that consequently need to be evaluated by a privacy mechanism. This paper shows that when compared to the existing context-dependent privacy policy languages, this number of rules (that are encoded using CPPL) decreases with an increasing number of user-defined situations and requestors that are represented by a small number of social relationship groups.

Devlic, Alisa

Abstract [en]

Mobile video content today generates more than half of the mobile data traffic.The increasing popularity of mobile video on demand services poses great challenges to mobile operators and content providers. Frontmost, how to reduce the mobile video traffic load, while delivering high quality video content to mobile users without perceived quality degradations for the same (or cheaper) price? Battery lifetime represents another key factor of a user’s Quality of Experience(QoE). A lot of device energy is consumed by mobile network signalling and data transmission over new generation mobile communication systems. This thesis focuses on: (1) reducing the size of the video that is delivered to the enduser in the maximum achievable video quality, thus optimizing the wireless network bandwidth and the user-perceived QoE, and (2) reducing the energy consumption of a mobile device that is associated to data transfer over the radio interface, thus increasing the device’s battery lifetime. The main contributions have been given in providing the Over-the-Top video optimization and delivery schemes and recommendations on tuning their parameters in order to minimize the bandwidth and energy consumption of mobile video delivery, while maximizing the predictable user-perceived QoE. By preventing the video to be prefetched on low data rates and tuning the datarate threshold according to statistical properties of available data rates, we show that 20-70% of energy cost can be reduced by opportunistic prefetching, depending on the user’s pattern of available data rates. The data rate values ordered in time that have a large amount of serial correlation and low noise variance, or low average valueand high peak-to-mean ratio, are likely to yield the highest energy gains from content prefetching. Moreover, we show that energy gains are the largest when the threshold data rate is set close to an average data rate, due to the highest availability of data rates around this value, and for longer sleep time between the prefetching periods, which increases the probability of moving away from the areas with low data rates. Next, we focus on QoE-aware mobile video delivery solutions that are more bandwidth efficient without compromising the user-perceived video quality. They deliver a video over a varying data rate channel that is optimized for viewing on a mobile device in the highest perceptual video quality that can be achieved in the given video and network conditions. An optimized video consists of short segments in the minimum resolutions that satisfy the target perceptual video quality and have up to 60% reduced size compared to the video in the corresponding fixed video resolution, without perceptible quality difference. The delivery is performed by on demand download, context-aware prefetching, or in real time using the QoE-aware adaptive video streaming that runs over Dynamic Adaptive video Streaming over HTTP (DASH). By limiting the maximum bitrates of the requested video segments and using the remaining throughput to prefetch optimized video segments in advance of playout, we show that QoE-aware adaptive video streaming maintains a more stable perceptual video quality than DASH despite the fluctuations of the channel bandwidth, while using fewer number of bits, which improves a user-perceived QoE. The results of this thesis can help operators and content providers to reduce their costs and provide more content to their users at the same (or cheaper) price.